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Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods

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  • Chao Chen
  • Kay Grennan
  • Judith Badner
  • Dandan Zhang
  • Elliot Gershon
  • Li Jin
  • Chunyu Liu

Abstract

The expression microarray is a frequently used approach to study gene expression on a genome-wide scale. However, the data produced by the thousands of microarray studies published annually are confounded by “batch effects,” the systematic error introduced when samples are processed in multiple batches. Although batch effects can be reduced by careful experimental design, they cannot be eliminated unless the whole study is done in a single batch. A number of programs are now available to adjust microarray data for batch effects prior to analysis. We systematically evaluated six of these programs using multiple measures of precision, accuracy and overall performance. ComBat, an Empirical Bayes method, outperformed the other five programs by most metrics. We also showed that it is essential to standardize expression data at the probe level when testing for correlation of expression profiles, due to a sizeable probe effect in microarray data that can inflate the correlation among replicates and unrelated samples.

Suggested Citation

  • Chao Chen & Kay Grennan & Judith Badner & Dandan Zhang & Elliot Gershon & Li Jin & Chunyu Liu, 2011. "Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-10, February.
    • Handle: RePEc:plo:pone00:0017238
    DOI: 10.1371/journal.pone.0017238
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    References listed on IDEAS

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    1. M. Kathleen Kerr, 2003. "Design Considerations for Efficient and Effective Microarray Studies," Biometrics, The International Biometric Society, vol. 59(4), pages 822-828, December.
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    1. Romain Banchereau & Alejandro Jordan-Villegas & Monica Ardura & Asuncion Mejias & Nicole Baldwin & Hui Xu & Elizabeth Saye & Jose Rossello-Urgell & Phuong Nguyen & Derek Blankenship & Clarence B Creec, 2012. "Host Immune Transcriptional Profiles Reflect the Variability in Clinical Disease Manifestations in Patients with Staphylococcus aureus Infections," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-11, April.
    2. Qi Su & Qin Liu & Raphaela Iris Lau & Jingwan Zhang & Zhilu Xu & Yun Kit Yeoh & Thomas W. H. Leung & Whitney Tang & Lin Zhang & Jessie Q. Y. Liang & Yuk Kam Yau & Jiaying Zheng & Chengyu Liu & Mengjin, 2022. "Faecal microbiome-based machine learning for multi-class disease diagnosis," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    3. Xia Qing & Thompson Jeffrey A. & Koestler Devin C., 2021. "Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE)," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 20(4-6), pages 101-119, December.
    4. Sean M Gibbons & Claire Duvallet & Eric J Alm, 2018. "Correcting for batch effects in case-control microbiome studies," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-17, April.
    5. Charlotte Soneson & Sarah Gerster & Mauro Delorenzi, 2014. "Batch Effect Confounding Leads to Strong Bias in Performance Estimates Obtained by Cross-Validation," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-13, June.
    6. Nazifa Ahmed Moumi & Badhan Das & Zarin Tasnim Promi & Nishat Anjum Bristy & Md Shamsuzzoha Bayzid, 2019. "Quartet-based inference of cell differentiation trees from ChIP-Seq histone modification data," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-25, September.
    7. Jacopo Umberto Verga & Matthew Huff & Diarmuid Owens & Bethany J. Wolf & Gary Hardiman, 2022. "Integrated Genomic and Bioinformatics Approaches to Identify Molecular Links between Endocrine Disruptors and Adverse Outcomes," IJERPH, MDPI, vol. 19(1), pages 1-24, January.
    8. Aline Talhouk & Stefan Kommoss & Robertson Mackenzie & Martin Cheung & Samuel Leung & Derek S Chiu & Steve E Kalloger & David G Huntsman & Stephanie Chen & Maria Intermaggio & Jacek Gronwald & Fong C , 2016. "Single-Patient Molecular Testing with NanoString nCounter Data Using a Reference-Based Strategy for Batch Effect Correction," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-18, April.
    9. Raihan K Uddin & Shiva M Singh, 2013. "Hippocampal Gene Expression Meta-Analysis Identifies Aging and Age-Associated Spatial Learning Impairment (ASLI) Genes and Pathways," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-16, July.
    10. Kejian Wang & Jiazhi Sun & Shufeng Zhou & Chunling Wan & Shengying Qin & Can Li & Lin He & Lun Yang, 2013. "Prediction of Drug-Target Interactions for Drug Repositioning Only Based on Genomic Expression Similarity," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-9, November.
    11. Samir Dou & Nathalie Villa-Vialaneix & Laurence Liaubet & Yvon Billon & Mario Giorgi & Hélène Gilbert & Jean-Luc Gourdine & Juliette Riquet & David Renaudeau, 2017. "1HNMR-Based metabolomic profiling method to develop plasma biomarkers for sensitivity to chronic heat stress in growing pigs," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-18, November.
    12. Christian Müller & Arne Schillert & Caroline Röthemeier & David-Alexandre Trégouët & Carole Proust & Harald Binder & Norbert Pfeiffer & Manfred Beutel & Karl J Lackner & Renate B Schnabel & Laurence T, 2016. "Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-23, June.

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